|
| Titre : |
Development of an intelligent management system: integration of machine learning and knowledge engineering techniques |
| Type de document : |
document électronique |
| Auteurs : |
Ferrial Diboune ; Ikbal Khouni, Auteur ; Mekroud,Noureddine, Directeur de thèse |
| Editeur : |
Setif:UFA |
| Année de publication : |
2025 |
| Importance : |
1 vol (88 f .) |
| Format : |
29 cm |
| Langues : |
Anglais (eng) |
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Informatique
Management system |
| Index. décimale : |
004 Informatique |
| Résumé : |
In modern industrial environments, ensuring high-quality and uninterrupted production
is a major daily challenge. With the goal of preserving the accumulated knowledge of
experts, which forms the "corporate memory"—the company’s true capital—this work
proposes an intelligent system that combines processes for reusing past experience, predicting
future needs, and capitalizing on domain knowledge to support effective corrective
and preventive maintenance. The system leverages previous interventions to suggest appropriate
solutions for current failures. Additionally, the developed software intelligently
adjusts the optimal stock levels of spare parts and adapts their logistics based on observed
usage, and finally facilitates task assignment to technicians according to the current context
of interventions. This IT solution is a modular and scalable platform developed from
real-world industrial maintenance scenarios, ensuring the progressive integration of new
experiences from resolved failures, to gradually enhance decision-making support capabilities. |
| Note de contenu : |
Sommaire
1 Maintenance, Inventory, and Workforce Management 10
1.1 Maintenance Management . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.1.1 Strategic Role of Maintenance in an Organization . . . . . . . . . . 10
1.1.2 Problem of Expert Knowledge Loss . . . . . . . . . . . . . . . . . . 11
1.1.3 Types of Maintenance Approaches . . . . . . . . . . . . . . . . . . . 12
1.2 Principle of Spare Parts and Stock Management . . . . . . . . . . . . . . . 13
1.2.1 The Challenges of Spare Parts Stock Management . . . . . . . . . . 14
1.2.2 Strategies for Spare Parts Stock Optimization . . . . . . . . . . . . 15
1.2.3 Stock Replenishment Methods . . . . . . . . . . . . . . . . . . . . . 15
1.2.4 Calculation of Safety Stock with Random Demand . . . . . . . . . 16
1.3 Human Resource Management . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.3.1 Workforce Coordination for Failure Resolution . . . . . . . . . . . . 17
1.3.2 Task Assignment and Workforce Scheduling Conflicts . . . . . . . . 17
1.3.3 Predictive Planning for Workforce Gaps . . . . . . . . . . . . . . . 18
1.4 Overview of Integrated Intelligent Maintenance System . . . . . . . . . . . 18
2 Theoretical Background and Key Concepts 21
Part 1: Knowledge Management . . . . . . . . . . . . . . . . . . . . . . . . 21
2.1 Definition and Importance of Knowledge Management . . . . . . . . . . . . 21
2.2 Knowledge Management System Cycle . . . . . . . . . . . . . . . . . . . . 22
2.3 Application of Knowledge Management . . . . . . . . . . . . . . . . . . . . 23
2.3.1 Key Aspects of KM . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.2 Structural Implications of Automating Knowledge Processes . . . . 25
2.4 Components of Knowledge Management System . . . . . . . . . . . . . . . 25
Part 2: Knowledge Engineering . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5 Knowledge Engineering Process . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5.1 Knowledge Engineering and Knowledge Management . . . . . . . . 27
2.6 Experience Feedback as Knowledge Engineering: The Role of Case-Based
Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.7 Expert Systems in Maintenance Management . . . . . . . . . . . . . . . . 27
2.8 Prioritization Rules and Resource Allocation . . . . . . . . . . . . . . . . . 28
2.8.1 Defining Priority Criteria for Maintenance Tasks . . . . . . . . . . 29
Part 3: Machine Learning and Analytical Methods . . . . . . . . . . . . 29
2.9 Knowledge Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.10 Extracting Knowledge from Historical Data . . . . . . . . . . . . . . . . . 29
2.10.1 Trend Analysis for Predictive Stock and Maintenance Management 30
2.10.2 Detecting Failure Patterns and Forecasting Resource Needs . . . . . 30
2.11 Embedding Predictive Models in Decision Systems . . . . . . . . . . . . . . 30
2.12 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.12.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.12.2 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.12.3 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.12.4 Reinforcement learning . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.13 Optimization Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.13.1 Heuristic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.13.2 Metaheuristic Algorithms . . . . . . . . . . . . . . . . . . . . . . . 39
2.13.3 ARIMA (AutoRegressive Integrated Moving Average) . . . . . . . . 39
2.13.4 SARIMA (Seasonal ARIMA) . . . . . . . . . . . . . . . . . . . . . 40
2.14 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3 Proposed Approach 43
Axe 1: Corrective Maintenance – Case-Based Reason- ing for Intelligent
Failure Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.1 The CBR Cycle: Retrieve, Reuse, Revise, Retain . . . . . . . . . . . . . . 43
3.1.1 Why CBR Suits Corrective Maintenance . . . . . . . . . . . . . . . 44
3.1.2 Domain Relevance of Experience-Based Reasoning . . . . . . . . . . 45
3.2 Similarity Measures for Failure Matching . . . . . . . . . . . . . . . . . . . 45
3.2.1 Similarity for Numerical Features . . . . . . . . . . . . . . . . . . . 45
3.2.2 Similarity for Textual Features . . . . . . . . . . . . . . . . . . . . . 46
3.2.3 Hybrid Retrieval Strategy . . . . . . . . . . . . . . . . . . . . . . . 47
3.3 K-Nearest Neighbors (KNN) for Case Retrieval . . . . . . . . . . . . . . . 47
3.3.1 Feature Representation and Preprocessing . . . . . . . . . . . . . . 47
3.3.2 KNN-Based Retrieval of Top 5 Similar Failure Cases . . . . . . . . 47
3.3.3 Columns Used in Similarity Matching . . . . . . . . . . . . . . . . . 48
3.3.4 Expert Assistance: Suggested Spare Parts and Human Resources . 48
3.4 Dimensionality Reduction and Failure Clustering . . . . . . . . . . . . . . 48
3.4.1 Knowledge Base Construction and Case Structuring . . . . . . . . . 48
3.4.2 Projecting New Failures via Principal Component Analysis . . . . 48
3.5 Sequential Pattern Mining for Failure Analysis . . . . . . . . . . . . . . . . 49
Axe 2: Preventive Maintenance – Forecasting, Rules, and Inventory
Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.6 Designing Preventive Maintenance Rules . . . . . . . . . . . . . . . . . . . 49
3.6.1 Setting Threshold Intervals for Temperature and Pressure . . . . . 49
3.6.2 Identifying Frequent Failures for Periodic Check-Ups . . . . . . . . 49
3.6.3 Collaborating with Experts Before Rule Validation . . . . . . . . . 49
3.6.4 Defining Weekly, Monthly, and Quarterly Check Rules . . . . . . . 49
3.7 Forecasting Spare Parts for Proactive Stock Management . . . . . . . . . . 50
3.7.1 Importance of Demand Forecasting for Inventory . . . . . . . . . . 50
3.7.2 Time Series Components: Trend, Seasonality, Noise . . . . . . . . . 50
3.7.3 SARIMA Model for Monthly Spare Part Forecasting . . . . . . . . 50
3.7.4 Aggregation Strategy and Forecast Horizons . . . . . . . . . . . . . 50
3.8 Forecasting Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.8.1 Mean Absolute Error (MAE) . . . . . . . . . . . . . . . . . . . . . 51
3.8.2 Root Mean Squared Error (RMSE) . . . . . . . . . . . . . . . . . . 51
3.9 Intelligent Inventory Control and Reorder Management . . . . . . . . . . . 51
3.9.1 Adjusting Reorder Points Based on Lead Time and Forecasts . . . . 51
3.9.2 Calculating Safety Stock and Alert Levels . . . . . . . . . . . . . . 51
3.9.3 Integrating Preventive Forecasts with Inventory Decisions . . . . . . 52
Axe 3: Experience-Based Maintenance System Loop . . . . . . . . . . . 52
3.10 Continuous Learning and Case Base Evolution . . . . . . . . . . . . . . . . 52
3.11 Human-in-the-Loop Decision Support . . . . . . . . . . . . . . . . . . . . . 53
3.12 System Architecture Overview . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 Implementation and Results Analysis 62
Part 1: Machine Failure and Maintenance . . . . . . . . . . . . . . . . . . 62
4.1 Dataset Structure and Class Diagram . . . . . . . . . . . . . . . . . . . . . 62
4.2 Description of the Machine Failure Dataset . . . . . . . . . . . . . . . . . . 63
4.3 Data Cleaning and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . 63
4.4 Feature Engineering and Encoding . . . . . . . . . . . . . . . . . . . . . . 64
4.5 Exploratory Data Analysis (EDA) . . . . . . . . . . . . . . . . . . . . . . . 64
4.6 Visualization of Similarity Using PCA . . . . . . . . . . . . . . . . . . . . 65
4.6.1 Need for Dimensionality Reduction . . . . . . . . . . . . . . . . . . 65
4.6.2 Principal Component Analysis (PCA) . . . . . . . . . . . . . . . . . 66
4.6.3 Interpretation of PCA Plot . . . . . . . . . . . . . . . . . . . . . . . 66
4.6.4 Explained Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.7 Analysis of PCA Inertia and Dimensional Space . . . . . . . . . . . . . . . 67
4.7.1 Dimensionality of the Hybrid Feature Space . . . . . . . . . . . . . 67
4.7.2 Inertia Explained by Principal Components . . . . . . . . . . . . . 68
4.7.3 Interpretation of Explained Inertia . . . . . . . . . . . . . . . . . . 68
4.8 Similarity Calculation Using KNN and Distance Metrics . . . . . . . . . . 69
4.9 Preventive Maintenance Planning Based on Failure Frequency Analysis . . 71
4.10 Sequential Pattern Mining and Rule-Based Failure Prediction . . . . . . . 72
4.11 New Machine Entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Part 2: Inventory Forecasting and Optimization . . . . . . . . . . . . . . 74
4.12 Description of the Spare Parts Inventory Dataset . . . . . . . . . . . . . . 74
4.13 Time Series Preparation and Preprocessing . . . . . . . . . . . . . . . . . . 75
4.14 Forecasting with SARIMA Model . . . . . . . . . . . . . . . . . . . . . . . 76
4.14.1 SARIMA Parameter Tuning . . . . . . . . . . . . . . . . . . . . . . 76
4.14.2 Model Fitting and Residual Diagnostics . . . . . . . . . . . . . . . 76
4.14.3 Forecast Accuracy Evaluation . . . . . . . . . . . . . . . . . . . . . 77
4.15 Inventory Optimization Based on Forecasted Demand . . . . . . . . . . . . 79
4.16 Results Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.16.1 Maintenance Optimization . . . . . . . . . . . . . . . . . . . . . . . 81
4.16.2 Inventory and Spare Part Planning . . . . . . . . . . . . . . . . . . 81
4.16.3 Human Resource Management . . . . . . . . . . . . . . . . . . . . . 82
4.16.4 Conclusion on Results Use . . . . . . . . . . . . . . . . . . . . . . . 82
|
| Côte titre : |
MAI/1051 |
Development of an intelligent management system: integration of machine learning and knowledge engineering techniques [document électronique] / Ferrial Diboune ; Ikbal Khouni, Auteur ; Mekroud,Noureddine, Directeur de thèse . - [S.l.] : Setif:UFA, 2025 . - 1 vol (88 f .) ; 29 cm. Langues : Anglais ( eng)
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Informatique
Management system |
| Index. décimale : |
004 Informatique |
| Résumé : |
In modern industrial environments, ensuring high-quality and uninterrupted production
is a major daily challenge. With the goal of preserving the accumulated knowledge of
experts, which forms the "corporate memory"—the company’s true capital—this work
proposes an intelligent system that combines processes for reusing past experience, predicting
future needs, and capitalizing on domain knowledge to support effective corrective
and preventive maintenance. The system leverages previous interventions to suggest appropriate
solutions for current failures. Additionally, the developed software intelligently
adjusts the optimal stock levels of spare parts and adapts their logistics based on observed
usage, and finally facilitates task assignment to technicians according to the current context
of interventions. This IT solution is a modular and scalable platform developed from
real-world industrial maintenance scenarios, ensuring the progressive integration of new
experiences from resolved failures, to gradually enhance decision-making support capabilities. |
| Note de contenu : |
Sommaire
1 Maintenance, Inventory, and Workforce Management 10
1.1 Maintenance Management . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.1.1 Strategic Role of Maintenance in an Organization . . . . . . . . . . 10
1.1.2 Problem of Expert Knowledge Loss . . . . . . . . . . . . . . . . . . 11
1.1.3 Types of Maintenance Approaches . . . . . . . . . . . . . . . . . . . 12
1.2 Principle of Spare Parts and Stock Management . . . . . . . . . . . . . . . 13
1.2.1 The Challenges of Spare Parts Stock Management . . . . . . . . . . 14
1.2.2 Strategies for Spare Parts Stock Optimization . . . . . . . . . . . . 15
1.2.3 Stock Replenishment Methods . . . . . . . . . . . . . . . . . . . . . 15
1.2.4 Calculation of Safety Stock with Random Demand . . . . . . . . . 16
1.3 Human Resource Management . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.3.1 Workforce Coordination for Failure Resolution . . . . . . . . . . . . 17
1.3.2 Task Assignment and Workforce Scheduling Conflicts . . . . . . . . 17
1.3.3 Predictive Planning for Workforce Gaps . . . . . . . . . . . . . . . 18
1.4 Overview of Integrated Intelligent Maintenance System . . . . . . . . . . . 18
2 Theoretical Background and Key Concepts 21
Part 1: Knowledge Management . . . . . . . . . . . . . . . . . . . . . . . . 21
2.1 Definition and Importance of Knowledge Management . . . . . . . . . . . . 21
2.2 Knowledge Management System Cycle . . . . . . . . . . . . . . . . . . . . 22
2.3 Application of Knowledge Management . . . . . . . . . . . . . . . . . . . . 23
2.3.1 Key Aspects of KM . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.2 Structural Implications of Automating Knowledge Processes . . . . 25
2.4 Components of Knowledge Management System . . . . . . . . . . . . . . . 25
Part 2: Knowledge Engineering . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5 Knowledge Engineering Process . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5.1 Knowledge Engineering and Knowledge Management . . . . . . . . 27
2.6 Experience Feedback as Knowledge Engineering: The Role of Case-Based
Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.7 Expert Systems in Maintenance Management . . . . . . . . . . . . . . . . 27
2.8 Prioritization Rules and Resource Allocation . . . . . . . . . . . . . . . . . 28
2.8.1 Defining Priority Criteria for Maintenance Tasks . . . . . . . . . . 29
Part 3: Machine Learning and Analytical Methods . . . . . . . . . . . . 29
2.9 Knowledge Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.10 Extracting Knowledge from Historical Data . . . . . . . . . . . . . . . . . 29
2.10.1 Trend Analysis for Predictive Stock and Maintenance Management 30
2.10.2 Detecting Failure Patterns and Forecasting Resource Needs . . . . . 30
2.11 Embedding Predictive Models in Decision Systems . . . . . . . . . . . . . . 30
2.12 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.12.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.12.2 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.12.3 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.12.4 Reinforcement learning . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.13 Optimization Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.13.1 Heuristic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.13.2 Metaheuristic Algorithms . . . . . . . . . . . . . . . . . . . . . . . 39
2.13.3 ARIMA (AutoRegressive Integrated Moving Average) . . . . . . . . 39
2.13.4 SARIMA (Seasonal ARIMA) . . . . . . . . . . . . . . . . . . . . . 40
2.14 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3 Proposed Approach 43
Axe 1: Corrective Maintenance – Case-Based Reason- ing for Intelligent
Failure Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.1 The CBR Cycle: Retrieve, Reuse, Revise, Retain . . . . . . . . . . . . . . 43
3.1.1 Why CBR Suits Corrective Maintenance . . . . . . . . . . . . . . . 44
3.1.2 Domain Relevance of Experience-Based Reasoning . . . . . . . . . . 45
3.2 Similarity Measures for Failure Matching . . . . . . . . . . . . . . . . . . . 45
3.2.1 Similarity for Numerical Features . . . . . . . . . . . . . . . . . . . 45
3.2.2 Similarity for Textual Features . . . . . . . . . . . . . . . . . . . . . 46
3.2.3 Hybrid Retrieval Strategy . . . . . . . . . . . . . . . . . . . . . . . 47
3.3 K-Nearest Neighbors (KNN) for Case Retrieval . . . . . . . . . . . . . . . 47
3.3.1 Feature Representation and Preprocessing . . . . . . . . . . . . . . 47
3.3.2 KNN-Based Retrieval of Top 5 Similar Failure Cases . . . . . . . . 47
3.3.3 Columns Used in Similarity Matching . . . . . . . . . . . . . . . . . 48
3.3.4 Expert Assistance: Suggested Spare Parts and Human Resources . 48
3.4 Dimensionality Reduction and Failure Clustering . . . . . . . . . . . . . . 48
3.4.1 Knowledge Base Construction and Case Structuring . . . . . . . . . 48
3.4.2 Projecting New Failures via Principal Component Analysis . . . . 48
3.5 Sequential Pattern Mining for Failure Analysis . . . . . . . . . . . . . . . . 49
Axe 2: Preventive Maintenance – Forecasting, Rules, and Inventory
Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.6 Designing Preventive Maintenance Rules . . . . . . . . . . . . . . . . . . . 49
3.6.1 Setting Threshold Intervals for Temperature and Pressure . . . . . 49
3.6.2 Identifying Frequent Failures for Periodic Check-Ups . . . . . . . . 49
3.6.3 Collaborating with Experts Before Rule Validation . . . . . . . . . 49
3.6.4 Defining Weekly, Monthly, and Quarterly Check Rules . . . . . . . 49
3.7 Forecasting Spare Parts for Proactive Stock Management . . . . . . . . . . 50
3.7.1 Importance of Demand Forecasting for Inventory . . . . . . . . . . 50
3.7.2 Time Series Components: Trend, Seasonality, Noise . . . . . . . . . 50
3.7.3 SARIMA Model for Monthly Spare Part Forecasting . . . . . . . . 50
3.7.4 Aggregation Strategy and Forecast Horizons . . . . . . . . . . . . . 50
3.8 Forecasting Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.8.1 Mean Absolute Error (MAE) . . . . . . . . . . . . . . . . . . . . . 51
3.8.2 Root Mean Squared Error (RMSE) . . . . . . . . . . . . . . . . . . 51
3.9 Intelligent Inventory Control and Reorder Management . . . . . . . . . . . 51
3.9.1 Adjusting Reorder Points Based on Lead Time and Forecasts . . . . 51
3.9.2 Calculating Safety Stock and Alert Levels . . . . . . . . . . . . . . 51
3.9.3 Integrating Preventive Forecasts with Inventory Decisions . . . . . . 52
Axe 3: Experience-Based Maintenance System Loop . . . . . . . . . . . 52
3.10 Continuous Learning and Case Base Evolution . . . . . . . . . . . . . . . . 52
3.11 Human-in-the-Loop Decision Support . . . . . . . . . . . . . . . . . . . . . 53
3.12 System Architecture Overview . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 Implementation and Results Analysis 62
Part 1: Machine Failure and Maintenance . . . . . . . . . . . . . . . . . . 62
4.1 Dataset Structure and Class Diagram . . . . . . . . . . . . . . . . . . . . . 62
4.2 Description of the Machine Failure Dataset . . . . . . . . . . . . . . . . . . 63
4.3 Data Cleaning and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . 63
4.4 Feature Engineering and Encoding . . . . . . . . . . . . . . . . . . . . . . 64
4.5 Exploratory Data Analysis (EDA) . . . . . . . . . . . . . . . . . . . . . . . 64
4.6 Visualization of Similarity Using PCA . . . . . . . . . . . . . . . . . . . . 65
4.6.1 Need for Dimensionality Reduction . . . . . . . . . . . . . . . . . . 65
4.6.2 Principal Component Analysis (PCA) . . . . . . . . . . . . . . . . . 66
4.6.3 Interpretation of PCA Plot . . . . . . . . . . . . . . . . . . . . . . . 66
4.6.4 Explained Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.7 Analysis of PCA Inertia and Dimensional Space . . . . . . . . . . . . . . . 67
4.7.1 Dimensionality of the Hybrid Feature Space . . . . . . . . . . . . . 67
4.7.2 Inertia Explained by Principal Components . . . . . . . . . . . . . 68
4.7.3 Interpretation of Explained Inertia . . . . . . . . . . . . . . . . . . 68
4.8 Similarity Calculation Using KNN and Distance Metrics . . . . . . . . . . 69
4.9 Preventive Maintenance Planning Based on Failure Frequency Analysis . . 71
4.10 Sequential Pattern Mining and Rule-Based Failure Prediction . . . . . . . 72
4.11 New Machine Entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Part 2: Inventory Forecasting and Optimization . . . . . . . . . . . . . . 74
4.12 Description of the Spare Parts Inventory Dataset . . . . . . . . . . . . . . 74
4.13 Time Series Preparation and Preprocessing . . . . . . . . . . . . . . . . . . 75
4.14 Forecasting with SARIMA Model . . . . . . . . . . . . . . . . . . . . . . . 76
4.14.1 SARIMA Parameter Tuning . . . . . . . . . . . . . . . . . . . . . . 76
4.14.2 Model Fitting and Residual Diagnostics . . . . . . . . . . . . . . . 76
4.14.3 Forecast Accuracy Evaluation . . . . . . . . . . . . . . . . . . . . . 77
4.15 Inventory Optimization Based on Forecasted Demand . . . . . . . . . . . . 79
4.16 Results Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.16.1 Maintenance Optimization . . . . . . . . . . . . . . . . . . . . . . . 81
4.16.2 Inventory and Spare Part Planning . . . . . . . . . . . . . . . . . . 81
4.16.3 Human Resource Management . . . . . . . . . . . . . . . . . . . . . 82
4.16.4 Conclusion on Results Use . . . . . . . . . . . . . . . . . . . . . . . 82
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| Côte titre : |
MAI/1051 |
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Ajouter le résultat dans votre panier Affiner la rechercheDevelopment of an intelligent management system: integration of machine learning and knowledge engineering techniques / Ferrial Diboune
